High-Dimensional Generalized Orthogonal Matching Pursuit With Singular Value Decomposition

Matching pursuit (MP) is an algorithm, which can reconstruct signal accurately, and is widely used in signal processing. However, MP algorithm has efficiency problem in processing large amount of data, such as five-dimensional (5-D) seismic data. Generalized orthogonal MP with singular value decomposition (SVD_GOMP) is an algorithm, which can improve the calculation efficiency a lot, and keeps the advantage of high accuracy. In this study, a redundant atom dictionary includes incident angles, and azimuth is built. Then, the 5-D seismic data are reconstructed efficiently and accurately by the SVD_GOMP algorithm. Compared with the traditional MP method, the proposed method decomposes the 5-D seismic data at the same time and recovers the angle information efficiently. The reconstructed results of synthetic and field data examples are utilized to demonstrate the feasibility, computational efficiency, and precision of the proposed method.